Neo‐liberalism, community, and police regionalization in Canada
Bibliographic record
Abstract
Purpose Neo‐liberal policies have had a profound effect on the organization of policing in Canada by rationalizing provincial and federal initiatives that off‐load policing costs to municipal and regional councils. This paper aims to comparatively analyze the effect of these initiatives on service delivery for regional versus non‐regional police services. Design/methodology/approach Four measures were used to assess efficiency: per capita cost, cost per criminal code offence, number of officers per 100,000 population, and number of support staff per 100,000 population. Three measures were used to assess effectiveness: violent crime clearance rate, property crime clearance rate, and total criminal code clearance rate. Findings Analysis of the data reveals that, despite claims surrounding regionalization, regional police services are not demonstrably any more effective or efficient than non‐regional services. Research limitations/implications Utilizes official crime data and police expenditure statistics. A national survey of police service delivery and citizen satisfaction is needed. Practical implications – These results can inform municipal and town council decisions about regional (or provincial contract) versus local police service provision. Originality/value – The first comprehensive comparative Canadian study on the efficiency and effectiveness of police regionalization. The article empirically challenges the purported relative effectiveness and efficiency of larger regional police services versus smaller non‐regional services in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".